[Comp-neuro] (2) Levels
schwaber at mail.dbi.tju.edu
Thu Aug 21 23:38:23 CEST 2008
_(2) Levels _
I like what several post's have said about the legitimacy of modeling at
different levels of scale/analysis. In the first place, despite all the
talk about multi-scale modeling, actual multi-scale is really hard and
rare. So working at level 'x' and claiming you really are laying the
groundwork for level 'y' (in the fullness of time!) can be disingenuous.
Denis Noble makes a wonderful point about the requirement in model
building at any level of analysis for top down causation to constrain
bottom up mechanisms. One of his favorite examples is that he would
never find pacemaker activity at the heart no matter how long he studied
cardiac myocyte biophysics. Sydney Brenner has called this 'middle out'
analysis reaching up for the functional behavior and down for the
In contradiction to these folks I understand that you Jim believe if you
constrain a (model) system from top-down, then that is what you will
surely find... that the system exhibits the phenomenon you
told/constrained it to do and that this result is meaningless. They (and
I from gene network experience) would not agree with you that this is
easy and automatic as you seem to believe, e.g. the claim that complex
models can "fit anything" sounds good but is not true. No matter, the
claims here are (a) the outcome you lightly dismiss is a great hoped for
outcome/starting point and (b) a model of a functional process can not
be developed from working at the level of the reduced data alone, it
requires the function to be explained or understood. The implication,
which is going to irritate you, is that the function is not inherent
(unique) in the collection of the parts. The "E word". Say no more.
A more general idea that I find useful (and that I think is common in
the SBML-ish world I mostly work in) is that models are best considered
as models of process, not of static structure/function. Even more
crucial, we evaluate our models as 'a model of what', are they heuristic
or useful (testable predictions) in the study of that - */_not at all
meant to be real_/*, or the real object. We see useful models of the
same larger system of many kinds, some as abstract as graphical patterns.
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